


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_torch
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		@slow
		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  XLMRobertaModel.from_pretrained("""xlm-roberta-base"""	)
					lowerCamelCase    		=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]	)
					# The dog is cute and lives in the garden house

					lowerCamelCase    		=  torch.Size((1, 12, 7_68)	)  # batch_size, sequence_length, embedding_vector_dim
					lowerCamelCase    		=  torch.tensor(
					    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
								lowerCamelCase    		=  model(A	)["""last_hidden_state"""].detach()
					self.assertEqual(output.shape		,      A	)
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1]		,      A		,      atol=1e-3	)	)







		@slow
		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  XLMRobertaModel.from_pretrained("""xlm-roberta-large"""	)
					lowerCamelCase    		=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]	)
					# The dog is cute and lives in the garden house

					lowerCamelCase    		=  torch.Size((1, 12, 10_24)	)  # batch_size, sequence_length, embedding_vector_dim
					lowerCamelCase    		=  torch.tensor(
					    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]	)
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
								lowerCamelCase    		=  model(A	)["""last_hidden_state"""].detach()
					self.assertEqual(output.shape		,      A	)
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1]		,      A		,      atol=1e-3	)	)








"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






